Image labeling involves assigning labels to image elements according to whether they depict background or foreground objects or for other tasks. For example, semantic image segmentation is a process whereby an image is parsed into semantically meaningful regions. For example, a medical image may need to be analyzed to enable body organs to be recognized. In another example, a video of a street scene may need to be parsed into regions which depict vehicles, pedestrians, road, and other objects.
Many existing approaches to image labeling are limited in terms of the accuracy of the results produced and the time and resources needed. Often two or more separate stages of processing are needed in order to give reasonable levels of accuracy and this introduces complexity and time costs.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known image labeling systems.